Comprehensive gene sequencing to identify progression predictors to muscle-invasive bladder cancer.
Bibliographic record
Abstract
570 Background: Over 8900 Canadians are diagnosed with bladder cancer every year, ranking it the fifth most frequent cancer. It can manifest as either non-muscle invasive bladder cancer (NMIBC) or muscle invasive bladder cancer (MIBC). The majority of patients initially receive a diagnosis of NMIBC, although high-grade NMIBC has a 50–70% recurrence rate and 10–30% chance at progressing to MIBC. The transition from high-grade NMIBC to MIBC is poorly understood, and there are currently no accurate biomarkers that predict disease progression. We propose a comprehensive molecular characterization to pinpoint specific copy number alterations (CNAs) related to either MIBC or NMIBC, and hence define the molecular development. Methods: This study analyzed a public dataset from MSKCC and 30 bladder cancer patient samples from Windsor Regional Hospital, both containing NMIBC and MIBC samples. Comprehensive gene sequencing was performed, and CNAs were obtained in over 500 common tumour gene panels. Results: Preliminary data from this study found MIBC may be predicted with 91% accuracy and 95% precision using CNA values of TP53, DDR2 and MLL2. In particular, MIBC correlates with gain of DDR2 or MLL2. Importantly, it has been demonstrated that high expression of DDR2 is associated with a worse prognosis. A panel of bladder carcinoma cell lines were used to validate the DDR2 findings. DDR2 values were quantified across the panels and corresponded with proliferation and invasiveness of cell lines. DDR2 was examined as a possible therapeutic target. Conclusions: Taken together, these findings provide insight to the pathogenesis of muscle invasion in bladder cancer. The potential to identify "genomic triggers" for the transition was facilitated by creating a genetic profile at these two stages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".